I am getting this error and cant understand why the issue is appearing. Below will be the code and error.

The result of the last printable workout

[-8.54582258e-01  9.83741381e+02] left

[   0.776281243  -160.77584028] right

The code error happens in make_coordinates and the line is

slope, intercept = line_parameters

Here's the full code:

import cv2

import numpy as np



vid = cv2.VideoCapture('carDriving.mp4')



def processImage(image):

    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    blur = cv2.GaussianBlur(gray, (5,5), 0)

    canny = cv2.Canny(blur, 50, 150)

    return canny



def region_of_interest(image):

    height = image.shape[0]

    polygons = np.array([

    [(200,height), (1200,height), (750,300)]

    ])

    mask = np.zeros_like(image)

    cv2.fillPoly(mask, polygons, 255)

    masked_image = cv2.bitwise_and(image, mask) 

    return masked_image



def display_lines(image, lines):

    line_image = np.zeros_like(image)

    if lines is not None:

        for line in lines:

            x1, y1, x2, y2 = line.reshape(4)

            cv2.line(line_image, (x1, y1), (x2, y2), (255,0,0), 10)

    return line_image



def average_slope_intercept(image, lines):

    left_fit = 

    right_fit = 

    if lines is not None:

        for line in lines:

            x1, y1, x2, y2 = line.reshape(4)

            parameters = np.polyfit((x1, x2), (y1, y2), 1)

            slope = parameters[0]

            intercept = parameters[1]

            if slope < 0:

                left_fit.append((slope, intercept))

            else:

                right_fit.append((slope, intercept))

        left_fit_average = np.average(left_fit, axis=0)

        right_fit_average = np.average(right_fit, axis=0)

        print(left_fit_average, 'left')

        print(right_fit_average, 'right')

        left_line = make_coordinates(image, left_fit_average)

        right_line = make_coordinates(image, right_fit_average)

        #return np.array([left_line, right_line])



def make_coordinates(image, line_parameters):

    slope, intercept = line_parameters

    y1 = image.shape[0]

    y2 = int(y1*3/5)

    x1 = int(y1 - intercept)/slope

    x1 = int(y2 - intercept)/slope

    return np.array([x1, y1, x2, y2])



while True:

    ret, frame = vid.read()

    grayFrame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    processed_image = processImage(frame)

    cropped_image = region_of_interest(processed_image)

    lines = cv2.HoughLinesP(cropped_image, 2, np.pi/180, 100, np.array(), minLineLength=40, maxLineGap=5)

    averaged_lines = average_slope_intercept(grayFrame, lines)

    line_image = display_lines(cropped_image,lines) 

    combo_image = cv2.addWeighted(grayFrame, .6, line_image, 1, 1)

    cv2.imshow('result', combo_image)

    print(lines)

    if cv2.waitKey(30) & 0xFF == ord('q'):

        break



vid.release()

cv2.destroyAllWindows()

and the complete error message:

Message=cannot unpack non-iterable numpy.float64 object

Source=C:UsersAndresourcereposSelfDrivingCarTestSelfDrivingCarTestSelfDrivingCarTest.py

  StackTrace:

  File "C:UsersAndresourcereposSelfDrivingCarTestSelfDrivingCarTestSelfDrivingCarTest.py", line 52, in make_coordinates

    slope, intercept = line_parameters

  File "C:UsersAndresourcereposSelfDrivingCarTestSelfDrivingCarTestSelfDrivingCarTest.py", line 47, in average_slope_intercept

    left_line = make_coordinates(image, left_fit_average)

  File "C:UsersAndresourcereposSelfDrivingCarTestSelfDrivingCarTestSelfDrivingCarTest.py", line 65, in <module>

    averaged_lines = average_slope_intercept(grayFrame, lines)

Now receiving another error, line 27, first error was fixed

Message=integer argument expected, got float

  Source=C:UsersAndresourcereposSelfDrivingCarTestSelfDrivingCarTestSelfDrivingCarTest.py

  StackTrace:

  File "C:UsersAndresourcereposSelfDrivingCarTestSelfDrivingCarTestSelfDrivingCarTest.py", line 27, in display_lines

    cv2.line(line_image, (x1, y1), (x2, y2), (255,0,0), 10)

  File "C:UsersAndresourcereposSelfDrivingCarTestSelfDrivingCarTestSelfDrivingCarTest.py", line 76, in <module>

    line_image = display_lines(cropped_image,averaged_lines)

I change line 27 to cv2.line(line_image, int(x1, y1), int(x2, y2), (255,0,0), 10) and get the following error

  Message='numpy.float64' object cannot be interpreted as an integer

  Source=C:UsersAndresourcereposSelfDrivingCarTestSelfDrivingCarTestSelfDrivingCarTest.py

  StackTrace:

  File "C:UsersAndresourcereposSelfDrivingCarTestSelfDrivingCarTestSelfDrivingCarTest.py", line 27, in display_lines

    cv2.line(line_image, int(x1, y1), int(x2, y2), (255,0,0), 10)

  File "C:UsersAndresourcereposSelfDrivingCarTestSelfDrivingCarTestSelfDrivingCarTest.py", line 76, in <module>

    line_image = display_lines(cropped_image,averaged_lines)

The problem

There's a case in your code where line_parameters can be a single value, np.nan, instead of a pair of (slope, intercept) values. If the slope of your fits is always > 0, then left_fit will end up being an empty list :

        if slope < 0:

            left_fit.append((slope, intercept))

        else:

            right_fit.append((slope, intercept))

The output of np.average run on an empty list is NaN:

np.average()

# output: np.nan

# also raises two warnings: "RuntimeWarning: Mean of empty slice." and 

#                           "RuntimeWarning: invalid value encountered in double_scalars"

Thus, in some cases left_fit_average = np.average(left_fit) == np.average() == np.nan. np.nan has a type of numpy.float64. Your code then calls:

left_line = make_coordinates(image, line_parameters=left_fit_average)

Thus, when the call to make_coordinates gets to the line:

slope, intercept = line_parameters

it's possible for line_parameters to be np.nan, in which case you get the error message about:

TypeError: 'numpy.float64' object is not iterable

A fix

You can fix the bug by making sure that sensible values get assigned to slope and intercept even if line_parameters=np.nan. You can accomplished this by wrapping the assignment line in a try... except clause:

try:

    slope, intercept = line_parameters

except TypeError:

    slope, intercept = 0,0

You'll have to decide if this behavior is correct for your needs.

Alternatively, you could prevent the average_slope_intercept function from calling make_coordinates in the first place when one of the x_fit values doesn't have anything interesting in it:

if left_fit:

    left_fit_average = np.average(left_fit, axis=0)

    print(left_fit_average, 'left')

    left_line = make_coordinates(image, left_fit_average)

if right_fit:

    right_fit_average = np.average(right_fit, axis=0)

    print(right_fit_average, 'right')

    right_line = make_coordinates(image, right_fit_average)